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"""
K-Nearest Neighbors Classifier setup.
Features:
- Uses `KNeighborsClassifier`.
- Works for binary and multi-class tasks.
- Default scoring: 'accuracy'.
Considerations:
- `n_neighbors`, `weights`, and `p` (Minkowski distance) are common parameters to tune.
"""
from sklearn.neighbors import KNeighborsClassifier
estimator = KNeighborsClassifier()
param_grid = {
'model__n_neighbors': [3, 5], # Reduced to two neighbor options
'model__weights': ['uniform'], # Focused on one weighting strategy
'model__p': [2], # Fixed to Euclidean distance
# Preprocessing params
#'preprocessor__num__imputer__strategy': ['mean'],
#'preprocessor__num__scaler__with_mean': [True],
#'preprocessor__num__scaler__with_std': [True],
}
default_scoring = 'accuracy'
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